Evidence map›Paper›PMID 34772935›Full record

ArticleNature communications2021

Fast alignment and preprocessing of chromatin profiles with Chromap.

Haowen Zhang, Li Song, Xiaotao Wang, Haoyu Cheng, Chenfei Wang, Clifford A Meyer, Tao Liu, Ming Tang, Srinivas Aluru, Feng Yue and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 166 papers.

0numbers the graph read from it
0cells of the map it votes in
166citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

166 citing papers in PubMed.

  1. Article
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  8. Telomere-to-telomere genome ofHorticulture research · 2026
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  10. Chromosome-Scale Genome of Zoonotic EyewormAnimals : an open access journal from MDPI · 2026
    Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

106 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Haowen Zhang *School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.ORCID 0000-0002-3513-4573
Li Song *Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
Xiaotao WangDepartment of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.ORCID 0000-0002-3531-2157
Haoyu ChengDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
Chenfei WangDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
Clifford A MeyerDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
Tao LiuDepartment of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.ORCID 0000-0002-8818-8313
Ming TangDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID 0000-0002-7739-8913
Srinivas AluruSchool of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.ORCID 0000-0003-4279-469X
Feng YueDepartment of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.ORCID 0000-0002-7954-5462
X Shirley LiuDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. xsliu.res@gmail.com.
Heng LiDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. hli@ds.dfci.harvard.edu.ORCID 0000-0003-4874-2874

Funding

Advanced computational methods in analyzing high-throughput sequencing dataR01HG010040 · NHGRI · DANA-FARBER CANCER INST · PI Heng Li · 2018 to 2026
$3.7M
Next Generation Computational Tools for Functional GenomicsR01HG011139 · NHGRI · DANA-FARBER CANCER INST · PI IRIZARRY, RAFAEL ANGEL · 2020 to 2024
$3.5M
Bioinformatics Technology to Characterize Tumor Infiltrating Immune RepertoiresU01CA226196 · NCI · DANA-FARBER CANCER INST · PI LI, HENG · 2018 to 2020
$1.3M
NCI NIH HHS U01 CA226196NHGRI NIH HHS R01 HG010040NHGRI NIH HHS R01 HG011139
6 · The paper itself

Abstract

As sequencing depth of chromatin studies continually grows deeper for sensitive profiling of regulatory elements or chromatin spatial structures, aligning and preprocessing of these sequencing data have become the bottleneck for analysis. Here we present Chromap, an ultrafast method for aligning and preprocessing high throughput chromatin profiles. Chromap is comparable to BWA-MEM and Bowtie2 in alignment accuracy and is over 10 times faster than traditional workflows on bulk ChIP-seq/Hi-C profiles and than 10x Genomics' CellRanger v2.0.0 pipeline on single-cell ATAC-seq profiles.

Indexed as

ChromatinChromatin Immunoprecipitation SequencingComputational BiologyGene Expression ProfilingGenomicsHumansRegulatory Sequences, Nucleic AcidSequence Analysis, DNASingle-Cell AnalysisChromatin

Identifiers

PMID34772935
PMCPMC8589834

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.